Overview

Dataset statistics

Number of variables33
Number of observations134804
Missing cells235490
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.9 MiB
Average record size in memory264.0 B

Variable types

Numeric15
Unsupported1
Categorical17

Warnings

application_type has constant value "Individual" Constant
desc has a high cardinality: 48034 distinct values High cardinality
earliest_cr_line has a high cardinality: 607 distinct values High cardinality
emp_title has a high cardinality: 83424 distinct values High cardinality
title has a high cardinality: 32326 distinct values High cardinality
zip_code has a high cardinality: 834 distinct values High cardinality
fico_range_high is highly correlated with fico_range_lowHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
installment is highly correlated with loan_amntHigh correlation
loan_amnt is highly correlated with installmentHigh correlation
grade is highly correlated with application_type and 1 other fieldsHigh correlation
addr_state is highly correlated with application_typeHigh correlation
term is highly correlated with application_typeHigh correlation
verification_status is highly correlated with application_typeHigh correlation
application_type is highly correlated with grade and 10 other fieldsHigh correlation
purpose is highly correlated with application_typeHigh correlation
emp_length is highly correlated with application_typeHigh correlation
home_ownership is highly correlated with application_typeHigh correlation
initial_list_status is highly correlated with application_typeHigh correlation
loan_status is highly correlated with application_typeHigh correlation
sub_grade is highly correlated with grade and 1 other fieldsHigh correlation
issue_d is highly correlated with application_typeHigh correlation
member_id has 134804 (100.0%) missing values Missing
desc has 86076 (63.9%) missing values Missing
emp_length has 5962 (4.4%) missing values Missing
emp_title has 8565 (6.4%) missing values Missing
pub_rec is highly skewed (γ1 = 24.06241939) Skewed
revol_bal is highly skewed (γ1 = 27.9368739) Skewed
desc is uniformly distributed Uniform
id has unique values Unique
member_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
mort_acc has 51653 (38.3%) zeros Zeros
pub_rec has 118805 (88.1%) zeros Zeros
pub_rec_bankruptcies has 120491 (89.4%) zeros Zeros

Reproduction

Analysis started2021-04-19 13:53:16.288120
Analysis finished2021-04-19 13:54:29.863297
Duration1 minute and 13.58 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct134804
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6289701.213
Minimum356706
Maximum10234817
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:30.035399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum356706
5-th percentile3153423.35
Q14525783.75
median6328041
Q37725887.75
95-th percentile9736300.05
Maximum10234817
Range9878111
Interquartile range (IQR)3200104

Descriptive statistics

Standard deviation2073468.969
Coefficient of variation (CV)0.3296609646
Kurtosis-0.9958781613
Mean6289701.213
Median Absolute Deviation (MAD)1716915.5
Skewness-0.01644913247
Sum8.478768823 × 1011
Variance4.299273566 × 1012
MonotocityNot monotonic
2021-04-19T09:54:30.203240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41963511
 
< 0.1%
56249761
 
< 0.1%
98274761
 
< 0.1%
57848941
 
< 0.1%
64155101
 
< 0.1%
56065551
 
< 0.1%
56167961
 
< 0.1%
43095531
 
< 0.1%
45661751
 
< 0.1%
91946591
 
< 0.1%
Other values (134794)134794
> 99.9%
ValueCountFrequency (%)
3567061
< 0.1%
3800411
< 0.1%
4423191
< 0.1%
4763261
< 0.1%
5469661
< 0.1%
ValueCountFrequency (%)
102348171
< 0.1%
102348141
< 0.1%
102348131
< 0.1%
102347961
< 0.1%
102347621
< 0.1%

member_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing134804
Missing (%)100.0%
Memory size1.0 MiB

loan_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Fully Paid
113780 
Charged Off
21024 

Length

Max length11
Median length10
Mean length10.15595976
Min length10

Characters and Unicode

Total characters1369064
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid
ValueCountFrequency (%)
Fully Paid113780
84.4%
Charged Off21024
 
15.6%
2021-04-19T09:54:30.462431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:30.550289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
paid113780
42.2%
fully113780
42.2%
off21024
 
7.8%
charged21024
 
7.8%

Most occurring characters

ValueCountFrequency (%)
l227560
16.6%
134804
9.8%
a134804
9.8%
d134804
9.8%
F113780
8.3%
u113780
8.3%
y113780
8.3%
P113780
8.3%
i113780
8.3%
f42048
 
3.1%
Other values (6)126144
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter964652
70.5%
Uppercase Letter269608
 
19.7%
Space Separator134804
 
9.8%

Most frequent character per category

ValueCountFrequency (%)
l227560
23.6%
a134804
14.0%
d134804
14.0%
u113780
11.8%
y113780
11.8%
i113780
11.8%
f42048
 
4.4%
h21024
 
2.2%
r21024
 
2.2%
g21024
 
2.2%
ValueCountFrequency (%)
F113780
42.2%
P113780
42.2%
C21024
 
7.8%
O21024
 
7.8%
ValueCountFrequency (%)
134804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1234260
90.2%
Common134804
 
9.8%

Most frequent character per script

ValueCountFrequency (%)
l227560
18.4%
a134804
10.9%
d134804
10.9%
F113780
9.2%
u113780
9.2%
y113780
9.2%
P113780
9.2%
i113780
9.2%
f42048
 
3.4%
C21024
 
1.7%
Other values (5)105120
8.5%
ValueCountFrequency (%)
134804
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1369064
100.0%

Most frequent character per block

ValueCountFrequency (%)
l227560
16.6%
134804
9.8%
a134804
9.8%
d134804
9.8%
F113780
8.3%
u113780
8.3%
y113780
8.3%
P113780
8.3%
i113780
8.3%
f42048
 
3.1%
Other values (6)126144
9.2%

addr_state
Categorical

HIGH CORRELATION

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
CA
21466 
NY
11151 
TX
10291 
FL
8857 
IL
 
5266
Other values (44)
77773 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters269608
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTX
2nd rowMI
3rd rowCO
4th rowCA
5th rowNC
ValueCountFrequency (%)
CA21466
 
15.9%
NY11151
 
8.3%
TX10291
 
7.6%
FL8857
 
6.6%
IL5266
 
3.9%
NJ5112
 
3.8%
PA4599
 
3.4%
OH4345
 
3.2%
GA4236
 
3.1%
VA4097
 
3.0%
Other values (39)55384
41.1%
2021-04-19T09:54:30.799961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca21466
 
15.9%
ny11151
 
8.3%
tx10291
 
7.6%
fl8857
 
6.6%
il5266
 
3.9%
nj5112
 
3.8%
pa4599
 
3.4%
oh4345
 
3.2%
ga4236
 
3.1%
va4097
 
3.0%
Other values (39)55384
41.1%

Most occurring characters

ValueCountFrequency (%)
A48435
18.0%
C32173
11.9%
N30114
11.2%
L17381
 
6.4%
T16059
 
6.0%
M15081
 
5.6%
I13919
 
5.2%
Y12723
 
4.7%
O12553
 
4.7%
X10291
 
3.8%
Other values (14)60879
22.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter269608
100.0%

Most frequent character per category

ValueCountFrequency (%)
A48435
18.0%
C32173
11.9%
N30114
11.2%
L17381
 
6.4%
T16059
 
6.0%
M15081
 
5.6%
I13919
 
5.2%
Y12723
 
4.7%
O12553
 
4.7%
X10291
 
3.8%
Other values (14)60879
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin269608
100.0%

Most frequent character per script

ValueCountFrequency (%)
A48435
18.0%
C32173
11.9%
N30114
11.2%
L17381
 
6.4%
T16059
 
6.0%
M15081
 
5.6%
I13919
 
5.2%
Y12723
 
4.7%
O12553
 
4.7%
X10291
 
3.8%
Other values (14)60879
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII269608
100.0%

Most frequent character per block

ValueCountFrequency (%)
A48435
18.0%
C32173
11.9%
N30114
11.2%
L17381
 
6.4%
T16059
 
6.0%
M15081
 
5.6%
I13919
 
5.2%
Y12723
 
4.7%
O12553
 
4.7%
X10291
 
3.8%
Other values (14)60879
22.6%

annual_inc
Real number (ℝ≥0)

Distinct12038
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73226.92184
Minimum6000
Maximum6100000
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:30.935519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6000
5-th percentile30000
Q145570.25
median64000
Q389000
95-th percentile148000
Maximum6100000
Range6094000
Interquartile range (IQR)43429.75

Descriptive statistics

Standard deviation48822.61326
Coefficient of variation (CV)0.6667303777
Kurtosis1808.506083
Mean73226.92184
Median Absolute Deviation (MAD)20133.5
Skewness18.85340526
Sum9871281971
Variance2383647565
MonotocityNot monotonic
2021-04-19T09:54:31.074216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600005178
 
3.8%
500004817
 
3.6%
650003859
 
2.9%
700003711
 
2.8%
400003707
 
2.7%
800003574
 
2.7%
450003501
 
2.6%
750003400
 
2.5%
550003370
 
2.5%
900002648
 
2.0%
Other values (12028)97039
72.0%
ValueCountFrequency (%)
60001
< 0.1%
70001
< 0.1%
72001
< 0.1%
75001
< 0.1%
76001
< 0.1%
ValueCountFrequency (%)
61000001
< 0.1%
20000002
< 0.1%
15100001
< 0.1%
13500001
< 0.1%
13000001
< 0.1%

application_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Individual
134804 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1348040
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual
ValueCountFrequency (%)
Individual134804
100.0%
2021-04-19T09:54:31.340875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:31.420997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
individual134804
100.0%

Most occurring characters

ValueCountFrequency (%)
d269608
20.0%
i269608
20.0%
I134804
10.0%
n134804
10.0%
v134804
10.0%
u134804
10.0%
a134804
10.0%
l134804
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1213236
90.0%
Uppercase Letter134804
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
d269608
22.2%
i269608
22.2%
n134804
11.1%
v134804
11.1%
u134804
11.1%
a134804
11.1%
l134804
11.1%
ValueCountFrequency (%)
I134804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1348040
100.0%

Most frequent character per script

ValueCountFrequency (%)
d269608
20.0%
i269608
20.0%
I134804
10.0%
n134804
10.0%
v134804
10.0%
u134804
10.0%
a134804
10.0%
l134804
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1348040
100.0%

Most frequent character per block

ValueCountFrequency (%)
d269608
20.0%
i269608
20.0%
I134804
10.0%
n134804
10.0%
v134804
10.0%
u134804
10.0%
a134804
10.0%
l134804
10.0%

desc
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct48034
Distinct (%)98.6%
Missing86076
Missing (%)63.9%
Memory size1.0 MiB
Borrower added on 01/14/13 > Debt consolidation<br>
 
6
Borrower added on 07/25/13 > Debt consolidation<br>
 
6
Borrower added on 12/10/13 > Debt consolidation<br>
 
5
Borrower added on 12/13/13 > Debt consolidation<br>
 
5
Borrower added on 08/19/13 > Debt consolidation.<br>
 
5
Other values (48029)
48701 

Length

Max length2365
Median length134
Mean length167.0350312
Min length1

Characters and Unicode

Total characters8139283
Distinct characters92
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47558 ?
Unique (%)97.6%

Sample

1st row Borrower added on 12/31/13 > Bought a new house, furniture, water softener, a second car, etc. Got our lives started and now a manageable monthly payment will help keep them going!<br>
2nd row Borrower added on 12/31/13 > Combining high interest credit cards to lower interest rate.<br>
3rd row Borrower added on 12/31/13 > I would like to use this money to payoff existing credit card debt and use the remaining about to purchase a used car that is fuel efficient.<br>
4th row Borrower added on 12/31/13 > I had some water main break and sewer replacement that ran up my Credit cards. I want to consolidate the Credit cards pay off one loan and refurbish my bathrooms.<br><br> Borrower added on 12/31/13 > I had two water main breaks one sewer and one clean water and the cost ran up credit cards expenditures. I want to consolidate the credit cards with a set payment and upgrade my two bathrooms and water heater.<br><br> Borrower added on 12/31/13 > Consolidate credet cards and upgrade bathrooms.<br><br> Borrower added on 12/31/13 > Consolidate credit cards and upgrade two bathrooms.I have been at this job for six years and the job before this one for 24 years. This will make my finances easier to manage. It will provide more efficient bathroom equipment and water heater.<br>
5th row Borrower added on 12/31/13 > While being in college there were expenses that I had to make. At the moment it seemed easy to buy thing on credit, but now that I'm full-time employee paying all credit cards seem impossible and it'll be great to make one consolidated payment to one firm with knowing its for a set amount of months.<br>
ValueCountFrequency (%)
Borrower added on 01/14/13 > Debt consolidation<br>6
 
< 0.1%
Borrower added on 07/25/13 > Debt consolidation<br>6
 
< 0.1%
Borrower added on 12/10/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 12/13/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 08/19/13 > Debt consolidation.<br>5
 
< 0.1%
Borrower added on 09/04/13 > debt consolidation<br>5
 
< 0.1%
Borrower added on 11/14/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 09/05/13 > Debt consolidation<br>5
 
< 0.1%
Borrower added on 08/06/13 > debt consolidation<br>5
 
< 0.1%
Borrower added on 09/19/13 > Debt consolidation<br>5
 
< 0.1%
Other values (48024)48676
36.1%
(Missing)86076
63.9%
2021-04-19T09:54:31.799891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
on64032
 
4.4%
to61174
 
4.2%
56295
 
3.9%
borrower55007
 
3.8%
added54788
 
3.8%
i42636
 
3.0%
and36536
 
2.5%
credit34996
 
2.4%
my34764
 
2.4%
a27956
 
1.9%
Other values (25565)973787
67.5%

Most occurring characters

ValueCountFrequency (%)
1507262
18.5%
e636955
 
7.8%
o568046
 
7.0%
r495087
 
6.1%
a479295
 
5.9%
t428072
 
5.3%
n404833
 
5.0%
d403289
 
5.0%
i320468
 
3.9%
s248782
 
3.1%
Other values (82)2647194
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5610117
68.9%
Space Separator1507262
 
18.5%
Decimal Number368773
 
4.5%
Uppercase Letter243704
 
3.0%
Other Punctuation219577
 
2.7%
Math Symbol180486
 
2.2%
Currency Symbol3219
 
< 0.1%
Dash Punctuation2944
 
< 0.1%
Close Punctuation1656
 
< 0.1%
Open Punctuation1518
 
< 0.1%
Other values (3)27
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
B57534
23.6%
I51933
21.3%
T24654
10.1%
C14243
 
5.8%
A10262
 
4.2%
D7825
 
3.2%
E7619
 
3.1%
L7437
 
3.1%
P7299
 
3.0%
O7224
 
3.0%
Other values (16)47674
19.6%
ValueCountFrequency (%)
e636955
11.4%
o568046
10.1%
r495087
 
8.8%
a479295
 
8.5%
t428072
 
7.6%
n404833
 
7.2%
d403289
 
7.2%
i320468
 
5.7%
s248782
 
4.4%
l228637
 
4.1%
Other values (16)1396653
24.9%
ValueCountFrequency (%)
/110948
50.5%
.72459
33.0%
,18796
 
8.6%
'6354
 
2.9%
!4975
 
2.3%
%2089
 
1.0%
;1897
 
0.9%
&1507
 
0.7%
:454
 
0.2%
?48
 
< 0.1%
Other values (2)50
 
< 0.1%
ValueCountFrequency (%)
1109814
29.8%
078784
21.4%
369865
18.9%
239840
 
10.8%
513050
 
3.5%
411959
 
3.2%
611914
 
3.2%
711632
 
3.2%
911631
 
3.2%
810284
 
2.8%
ValueCountFrequency (%)
>117398
65.0%
<62704
34.7%
+284
 
0.2%
~73
 
< 0.1%
|18
 
< 0.1%
=9
 
< 0.1%
ValueCountFrequency (%)
(1511
99.5%
[6
 
0.4%
{1
 
0.1%
ValueCountFrequency (%)
)1649
99.6%
]6
 
0.4%
}1
 
0.1%
ValueCountFrequency (%)
1507262
100.0%
ValueCountFrequency (%)
-2944
100.0%
ValueCountFrequency (%)
$3219
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
`4
100.0%
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5853821
71.9%
Common2285462
 
28.1%

Most frequent character per script

ValueCountFrequency (%)
e636955
 
10.9%
o568046
 
9.7%
r495087
 
8.5%
a479295
 
8.2%
t428072
 
7.3%
n404833
 
6.9%
d403289
 
6.9%
i320468
 
5.5%
s248782
 
4.2%
l228637
 
3.9%
Other values (42)1640357
28.0%
ValueCountFrequency (%)
1507262
65.9%
>117398
 
5.1%
/110948
 
4.9%
1109814
 
4.8%
078784
 
3.4%
.72459
 
3.2%
369865
 
3.1%
<62704
 
2.7%
239840
 
1.7%
,18796
 
0.8%
Other values (30)97592
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII8139283
100.0%

Most frequent character per block

ValueCountFrequency (%)
1507262
18.5%
e636955
 
7.8%
o568046
 
7.0%
r495087
 
6.1%
a479295
 
5.9%
t428072
 
5.3%
n404833
 
5.0%
d403289
 
5.0%
i320468
 
3.9%
s248782
 
3.1%
Other values (82)2647194
32.5%

dti
Real number (ℝ≥0)

Distinct3495
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.21772358
Minimum0
Maximum34.99
Zeros44
Zeros (%)< 0.1%
Memory size1.0 MiB
2021-04-19T09:54:31.955192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.22
Q111.47
median16.89
Q322.8
95-th percentile30.17
Maximum34.99
Range34.99
Interquartile range (IQR)11.33

Descriptive statistics

Standard deviation7.595662054
Coefficient of variation (CV)0.4411536762
Kurtosis-0.6889768515
Mean17.21772358
Median Absolute Deviation (MAD)5.65
Skewness0.1347048849
Sum2321018.01
Variance57.69408204
MonotocityNot monotonic
2021-04-19T09:54:32.098297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.4149
 
0.1%
18110
 
0.1%
15.6104
 
0.1%
16.8103
 
0.1%
19.2102
 
0.1%
20.4100
 
0.1%
21.698
 
0.1%
12.7298
 
0.1%
13.298
 
0.1%
1297
 
0.1%
Other values (3485)133745
99.2%
ValueCountFrequency (%)
044
< 0.1%
0.012
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.062
 
< 0.1%
ValueCountFrequency (%)
34.998
< 0.1%
34.9811
< 0.1%
34.9711
< 0.1%
34.9610
< 0.1%
34.9511
< 0.1%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct607
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Oct-2000
 
1122
Oct-2001
 
1065
Oct-1999
 
1033
Nov-1999
 
1026
Nov-2000
 
1015
Other values (602)
129543 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1078432
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)< 0.1%

Sample

1st rowSep-2003
2nd rowOct-1986
3rd rowNov-1997
4th rowNov-1994
5th rowDec-2009
ValueCountFrequency (%)
Oct-20001122
 
0.8%
Oct-20011065
 
0.8%
Oct-19991033
 
0.8%
Nov-19991026
 
0.8%
Nov-20001015
 
0.8%
Dec-20001007
 
0.7%
Aug-2000970
 
0.7%
Nov-1998948
 
0.7%
Jan-2001944
 
0.7%
Dec-1999942
 
0.7%
Other values (597)124732
92.5%
2021-04-19T09:54:32.697242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oct-20001122
 
0.8%
oct-20011065
 
0.8%
oct-19991033
 
0.8%
nov-19991026
 
0.8%
nov-20001015
 
0.8%
dec-20001007
 
0.7%
aug-2000970
 
0.7%
nov-1998948
 
0.7%
jan-2001944
 
0.7%
dec-1999942
 
0.7%
Other values (597)124732
92.5%

Most occurring characters

ValueCountFrequency (%)
9148900
13.8%
-134804
 
12.5%
0131115
 
12.2%
191159
 
8.5%
270922
 
6.6%
e34512
 
3.2%
u32812
 
3.0%
J32753
 
3.0%
a32582
 
3.0%
828932
 
2.7%
Other values (23)339941
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number539216
50.0%
Lowercase Letter269608
25.0%
Uppercase Letter134804
 
12.5%
Dash Punctuation134804
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e34512
12.8%
u32812
12.2%
a32582
12.1%
c25523
9.5%
n22009
8.2%
p21363
7.9%
r20121
7.5%
t13070
 
4.8%
o12258
 
4.5%
v12258
 
4.5%
Other values (4)43100
16.0%
ValueCountFrequency (%)
9148900
27.6%
0131115
24.3%
191159
16.9%
270922
13.2%
828932
 
5.4%
716011
 
3.0%
613880
 
2.6%
412887
 
2.4%
512850
 
2.4%
312560
 
2.3%
ValueCountFrequency (%)
J32753
24.3%
A21315
15.8%
M20896
15.5%
O13070
 
9.7%
D12453
 
9.2%
N12258
 
9.1%
S11793
 
8.7%
F10266
 
7.6%
ValueCountFrequency (%)
-134804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common674020
62.5%
Latin404412
37.5%

Most frequent character per script

ValueCountFrequency (%)
e34512
 
8.5%
u32812
 
8.1%
J32753
 
8.1%
a32582
 
8.1%
c25523
 
6.3%
n22009
 
5.4%
p21363
 
5.3%
A21315
 
5.3%
M20896
 
5.2%
r20121
 
5.0%
Other values (12)140526
34.7%
ValueCountFrequency (%)
9148900
22.1%
-134804
20.0%
0131115
19.5%
191159
13.5%
270922
10.5%
828932
 
4.3%
716011
 
2.4%
613880
 
2.1%
412887
 
1.9%
512850
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1078432
100.0%

Most frequent character per block

ValueCountFrequency (%)
9148900
13.8%
-134804
 
12.5%
0131115
 
12.2%
191159
 
8.5%
270922
 
6.6%
e34512
 
3.2%
u32812
 
3.0%
J32753
 
3.0%
a32582
 
3.0%
828932
 
2.7%
Other values (23)339941
31.5%

emp_length
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing5962
Missing (%)4.4%
Memory size1.0 MiB
10+ years
45799 
2 years
11239 
3 years
10095 
5 years
9727 
< 1 year
9083 
Other values (6)
42899 

Length

Max length9
Median length7
Mean length7.721030409
Min length6

Characters and Unicode

Total characters994793
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 years
2nd row10+ years
3rd row10+ years
4th row5 years
5th row4 years
ValueCountFrequency (%)
10+ years45799
34.0%
2 years11239
 
8.3%
3 years10095
 
7.5%
5 years9727
 
7.2%
< 1 year9083
 
6.7%
6 years8175
 
6.1%
7 years8173
 
6.1%
1 year7782
 
5.8%
4 years6884
 
5.1%
8 years6667
 
4.9%
(Missing)5962
 
4.4%
2021-04-19T09:54:32.975336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years111977
42.0%
1045799
17.2%
116865
 
6.3%
year16865
 
6.3%
211239
 
4.2%
310095
 
3.8%
59727
 
3.6%
9083
 
3.4%
68175
 
3.1%
78173
 
3.1%
Other values (3)18769
 
7.0%

Most occurring characters

ValueCountFrequency (%)
137925
13.9%
y128842
13.0%
e128842
13.0%
a128842
13.0%
r128842
13.0%
s111977
11.3%
162664
6.3%
045799
 
4.6%
+45799
 
4.6%
211239
 
1.1%
Other values (8)64022
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter627345
63.1%
Decimal Number174641
 
17.6%
Space Separator137925
 
13.9%
Math Symbol54882
 
5.5%

Most frequent character per category

ValueCountFrequency (%)
162664
35.9%
045799
26.2%
211239
 
6.4%
310095
 
5.8%
59727
 
5.6%
68175
 
4.7%
78173
 
4.7%
46884
 
3.9%
86667
 
3.8%
95218
 
3.0%
ValueCountFrequency (%)
y128842
20.5%
e128842
20.5%
a128842
20.5%
r128842
20.5%
s111977
17.8%
ValueCountFrequency (%)
+45799
83.4%
<9083
 
16.6%
ValueCountFrequency (%)
137925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin627345
63.1%
Common367448
36.9%

Most frequent character per script

ValueCountFrequency (%)
137925
37.5%
162664
17.1%
045799
 
12.5%
+45799
 
12.5%
211239
 
3.1%
310095
 
2.7%
59727
 
2.6%
<9083
 
2.5%
68175
 
2.2%
78173
 
2.2%
Other values (3)18769
 
5.1%
ValueCountFrequency (%)
y128842
20.5%
e128842
20.5%
a128842
20.5%
r128842
20.5%
s111977
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII994793
100.0%

Most frequent character per block

ValueCountFrequency (%)
137925
13.9%
y128842
13.0%
e128842
13.0%
a128842
13.0%
r128842
13.0%
s111977
11.3%
162664
6.3%
045799
 
4.6%
+45799
 
4.6%
211239
 
1.1%
Other values (8)64022
6.4%

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct83424
Distinct (%)66.1%
Missing8565
Missing (%)6.4%
Memory size1.0 MiB
Teacher
 
832
Manager
 
666
RN
 
388
Registered Nurse
 
356
Supervisor
 
304
Other values (83419)
123693 

Length

Max length42
Median length17
Mean length17.57868804
Min length1

Characters and Unicode

Total characters2219116
Distinct characters110
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73605 ?
Unique (%)58.3%

Sample

1st rowSystems Engineer
2nd rowTeam Leadern Customer Ops & Systems
3rd rowLTC
4th rowArea Sales Manager
5th rowProject Manager
ValueCountFrequency (%)
Teacher832
 
0.6%
Manager666
 
0.5%
RN388
 
0.3%
Registered Nurse356
 
0.3%
Supervisor304
 
0.2%
US Army287
 
0.2%
Project Manager256
 
0.2%
Sales218
 
0.2%
Bank of America216
 
0.2%
Office Manager210
 
0.2%
Other values (83414)122506
90.9%
(Missing)8565
 
6.4%
2021-04-19T09:54:33.498245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of8009
 
2.5%
manager6757
 
2.1%
inc6480
 
2.1%
3320
 
1.1%
center2493
 
0.8%
county2368
 
0.8%
services2150
 
0.7%
school2115
 
0.7%
medical2115
 
0.7%
hospital2072
 
0.7%
Other values (36606)277255
88.0%

Most occurring characters

ValueCountFrequency (%)
e197644
 
8.9%
193689
 
8.7%
a152294
 
6.9%
r147280
 
6.6%
n139193
 
6.3%
i137010
 
6.2%
t129590
 
5.8%
o128482
 
5.8%
s100848
 
4.5%
c80876
 
3.6%
Other values (100)812210
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1626743
73.3%
Uppercase Letter371201
 
16.7%
Space Separator193689
 
8.7%
Other Punctuation21516
 
1.0%
Decimal Number2744
 
0.1%
Dash Punctuation2431
 
0.1%
Open Punctuation359
 
< 0.1%
Close Punctuation348
 
< 0.1%
Math Symbol48
 
< 0.1%
Control21
 
< 0.1%
Other values (7)16
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
S42930
 
11.6%
C41268
 
11.1%
A30497
 
8.2%
M23598
 
6.4%
I20835
 
5.6%
P20159
 
5.4%
T19896
 
5.4%
E18411
 
5.0%
D17401
 
4.7%
R17197
 
4.6%
Other values (19)119009
32.1%
ValueCountFrequency (%)
e197644
12.1%
a152294
9.4%
r147280
9.1%
n139193
8.6%
i137010
8.4%
t129590
 
8.0%
o128482
 
7.9%
s100848
 
6.2%
c80876
 
5.0%
l78778
 
4.8%
Other values (17)334748
20.6%
ValueCountFrequency (%)
.9187
42.7%
,4977
23.1%
&3524
 
16.4%
/2119
 
9.8%
'1545
 
7.2%
#73
 
0.3%
:22
 
0.1%
!17
 
0.1%
"14
 
0.1%
\13
 
0.1%
Other values (4)25
 
0.1%
ValueCountFrequency (%)
1551
20.1%
2448
16.3%
3405
14.8%
0256
9.3%
4248
9.0%
5203
 
7.4%
6180
 
6.6%
9170
 
6.2%
7156
 
5.7%
8127
 
4.6%
ValueCountFrequency (%)
€6
28.6%
ƒ5
23.8%
™2
 
9.5%
š2
 
9.5%
“1
 
4.8%
’1
 
4.8%
†1
 
4.8%
…1
 
4.8%
œ1
 
4.8%
‚1
 
4.8%
ValueCountFrequency (%)
+35
72.9%
|9
 
18.8%
~2
 
4.2%
¬1
 
2.1%
±1
 
2.1%
ValueCountFrequency (%)
(358
99.7%
[1
 
0.3%
ValueCountFrequency (%)
)347
99.7%
]1
 
0.3%
ValueCountFrequency (%)
$3
60.0%
¢2
40.0%
ValueCountFrequency (%)
²1
50.0%
³1
50.0%
ValueCountFrequency (%)
193689
100.0%
ValueCountFrequency (%)
-2431
100.0%
ValueCountFrequency (%)
­1
100.0%
ValueCountFrequency (%)
`4
100.0%
ValueCountFrequency (%)
©1
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
«1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1997944
90.0%
Common221172
 
10.0%

Most frequent character per script

ValueCountFrequency (%)
e197644
 
9.9%
a152294
 
7.6%
r147280
 
7.4%
n139193
 
7.0%
i137010
 
6.9%
t129590
 
6.5%
o128482
 
6.4%
s100848
 
5.0%
c80876
 
4.0%
l78778
 
3.9%
Other values (46)705949
35.3%
ValueCountFrequency (%)
193689
87.6%
.9187
 
4.2%
,4977
 
2.3%
&3524
 
1.6%
-2431
 
1.1%
/2119
 
1.0%
'1545
 
0.7%
1551
 
0.2%
2448
 
0.2%
3405
 
0.2%
Other values (44)2296
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2219060
> 99.9%
None56
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
e197644
 
8.9%
193689
 
8.7%
a152294
 
6.9%
r147280
 
6.6%
n139193
 
6.3%
i137010
 
6.2%
t129590
 
5.8%
o128482
 
5.8%
s100848
 
4.5%
c80876
 
3.6%
Other values (77)812154
36.6%
ValueCountFrequency (%)
Ã13
23.2%
â6
10.7%
€6
10.7%
ƒ5
 
8.9%
Â5
 
8.9%
™2
 
3.6%
¢2
 
3.6%
š2
 
3.6%
­1
 
1.8%
©1
 
1.8%
Other values (13)13
23.2%

fico_range_high
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.9989763
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:33.642508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1679
median694
Q3714
95-th percentile754
Maximum850
Range186
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.76356342
Coefficient of variation (CV)0.04114965027
Kurtosis2.448939714
Mean698.9989763
Median Absolute Deviation (MAD)15
Skewness1.372036164
Sum94227858
Variance827.3425803
MonotocityNot monotonic
2021-04-19T09:54:33.778421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
67411378
 
8.4%
68411205
 
8.3%
67910601
 
7.9%
66910512
 
7.8%
69410319
 
7.7%
68910213
 
7.6%
6649642
 
7.2%
6999449
 
7.0%
7048416
 
6.2%
7097605
 
5.6%
Other values (28)35464
26.3%
ValueCountFrequency (%)
6649642
7.2%
66910512
7.8%
67411378
8.4%
67910601
7.9%
68411205
8.3%
ValueCountFrequency (%)
85012
 
< 0.1%
84419
 
< 0.1%
83924
 
< 0.1%
83456
< 0.1%
82986
0.1%

fico_range_low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694.9988873
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:33.922614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1675
median690
Q3710
95-th percentile750
Maximum845
Range185
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.76309763
Coefficient of variation (CV)0.04138581825
Kurtosis2.447536123
Mean694.9988873
Median Absolute Deviation (MAD)15
Skewness1.3718579
Sum93688630
Variance827.3157855
MonotocityNot monotonic
2021-04-19T09:54:34.052273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
67011378
 
8.4%
68011205
 
8.3%
67510601
 
7.9%
66510512
 
7.8%
69010319
 
7.7%
68510213
 
7.6%
6609642
 
7.2%
6959449
 
7.0%
7008416
 
6.2%
7057605
 
5.6%
Other values (28)35464
26.3%
ValueCountFrequency (%)
6609642
7.2%
66510512
7.8%
67011378
8.4%
67510601
7.9%
68011205
8.3%
ValueCountFrequency (%)
84512
 
< 0.1%
84019
 
< 0.1%
83524
 
< 0.1%
83056
< 0.1%
82586
0.1%

grade
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
B
44115 
C
38130 
D
20566 
A
17679 
E
9059 
Other values (2)
5255 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134804
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowA
5th rowB
ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%
2021-04-19T09:54:34.338117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:34.423314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b44115
32.7%
c38130
28.3%
d20566
15.3%
a17679
13.1%
e9059
 
6.7%
f4392
 
3.3%
g863
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter134804
100.0%

Most frequent character per category

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin134804
100.0%

Most frequent character per script

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII134804
100.0%

Most frequent character per block

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%

home_ownership
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
MORTGAGE
72061 
RENT
51495 
OWN
11248 

Length

Max length8
Median length8
Mean length6.054805495
Min length3

Characters and Unicode

Total characters816212
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMORTGAGE
2nd rowOWN
3rd rowMORTGAGE
4th rowMORTGAGE
5th rowRENT
ValueCountFrequency (%)
MORTGAGE72061
53.5%
RENT51495
38.2%
OWN11248
 
8.3%
2021-04-19T09:54:34.702767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:34.795198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage72061
53.5%
rent51495
38.2%
own11248
 
8.3%

Most occurring characters

ValueCountFrequency (%)
G144122
17.7%
R123556
15.1%
T123556
15.1%
E123556
15.1%
O83309
10.2%
M72061
8.8%
A72061
8.8%
N62743
7.7%
W11248
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter816212
100.0%

Most frequent character per category

ValueCountFrequency (%)
G144122
17.7%
R123556
15.1%
T123556
15.1%
E123556
15.1%
O83309
10.2%
M72061
8.8%
A72061
8.8%
N62743
7.7%
W11248
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin816212
100.0%

Most frequent character per script

ValueCountFrequency (%)
G144122
17.7%
R123556
15.1%
T123556
15.1%
E123556
15.1%
O83309
10.2%
M72061
8.8%
A72061
8.8%
N62743
7.7%
W11248
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII816212
100.0%

Most frequent character per block

ValueCountFrequency (%)
G144122
17.7%
R123556
15.1%
T123556
15.1%
E123556
15.1%
O83309
10.2%
M72061
8.8%
A72061
8.8%
N62743
7.7%
W11248
 
1.4%

initial_list_status
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
f
98892 
w
35912 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134804
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd roww
3rd rowf
4th roww
5th rowf
ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%
2021-04-19T09:54:35.038429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:35.121005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%

Most occurring characters

ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter134804
100.0%

Most frequent character per category

ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
Latin134804
100.0%

Most frequent character per script

ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII134804
100.0%

Most frequent character per block

ValueCountFrequency (%)
f98892
73.4%
w35912
 
26.6%

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24312
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.3941477
Minimum4.93
Maximum1408.13
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:35.222787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile132.84
Q1280.82
median404.3
Q3587.34
95-th percentile921.85
Maximum1408.13
Range1403.2
Interquartile range (IQR)306.52

Descriptive statistics

Standard deviation240.7709287
Coefficient of variation (CV)0.5322149502
Kurtosis0.7583653277
Mean452.3941477
Median Absolute Deviation (MAD)145.66
Skewness0.9037179921
Sum60984540.68
Variance57970.64013
MonotocityNot monotonic
2021-04-19T09:54:35.354309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337.47403
 
0.3%
635.07378
 
0.3%
317.54372
 
0.3%
332.72371
 
0.3%
343.39359
 
0.3%
328.06357
 
0.3%
332.1351
 
0.3%
476.3308
 
0.2%
625.81303
 
0.2%
492.08301
 
0.2%
Other values (24302)131301
97.4%
ValueCountFrequency (%)
4.931
< 0.1%
23.261
< 0.1%
25.861
< 0.1%
27.851
< 0.1%
28.822
< 0.1%
ValueCountFrequency (%)
1408.131
 
< 0.1%
1407.011
 
< 0.1%
1406.454
< 0.1%
1402.172
< 0.1%
1396.791
 
< 0.1%

int_rate
Real number (ℝ≥0)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.53162777
Minimum6
Maximum26.06
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:35.519766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.62
Q111.14
median14.33
Q317.56
95-th percentile22.47
Maximum26.06
Range20.06
Interquartile range (IQR)6.42

Descriptive statistics

Standard deviation4.437451623
Coefficient of variation (CV)0.3053650763
Kurtosis-0.4702868643
Mean14.53162777
Median Absolute Deviation (MAD)3.19
Skewness0.2424329856
Sum1958921.55
Variance19.69097691
MonotocityNot monotonic
2021-04-19T09:54:35.663887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.95001
 
3.7%
14.334872
 
3.6%
13.114647
 
3.4%
12.124445
 
3.3%
11.144292
 
3.2%
7.93444
 
2.6%
15.83425
 
2.5%
11.993384
 
2.5%
16.293265
 
2.4%
10.993185
 
2.4%
Other values (90)94844
70.4%
ValueCountFrequency (%)
629
 
< 0.1%
6.032569
1.9%
6.622144
1.6%
6.97397
 
0.3%
7.623155
2.3%
ValueCountFrequency (%)
26.0652
 
< 0.1%
25.9962
 
< 0.1%
25.89123
0.1%
25.83149
0.1%
25.8188
0.1%

issue_d
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Dec-2013
15012 
Nov-2013
14720 
Oct-2013
14127 
Sep-2013
12987 
Aug-2013
12674 
Other values (7)
65284 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1078432
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-2013
2nd rowDec-2013
3rd rowDec-2013
4th rowDec-2013
5th rowDec-2013
ValueCountFrequency (%)
Dec-201315012
11.1%
Nov-201314720
10.9%
Oct-201314127
10.5%
Sep-201312987
9.6%
Aug-201312674
9.4%
Jul-201311910
8.8%
Jun-201310899
8.1%
May-201310350
7.7%
Apr-20139419
7.0%
Mar-20138273
6.1%
Other values (2)14433
10.7%
2021-04-19T09:54:35.974190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-201315012
11.1%
nov-201314720
10.9%
oct-201314127
10.5%
sep-201312987
9.6%
aug-201312674
9.4%
jul-201311910
8.8%
jun-201310899
8.1%
may-201310350
7.7%
apr-20139419
7.0%
mar-20138273
6.1%
Other values (2)14433
10.7%

Most occurring characters

ValueCountFrequency (%)
-134804
12.5%
2134804
12.5%
0134804
12.5%
1134804
12.5%
3134804
12.5%
e35560
 
3.3%
u35483
 
3.3%
J29681
 
2.8%
c29139
 
2.7%
a25495
 
2.4%
Other values (17)249054
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number539216
50.0%
Lowercase Letter269608
25.0%
Uppercase Letter134804
 
12.5%
Dash Punctuation134804
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e35560
13.2%
u35483
13.2%
c29139
10.8%
a25495
9.5%
p22406
8.3%
n17771
6.6%
r17692
6.6%
o14720
 
5.5%
v14720
 
5.5%
t14127
 
5.2%
Other values (4)42495
15.8%
ValueCountFrequency (%)
J29681
22.0%
A22093
16.4%
M18623
13.8%
D15012
11.1%
N14720
10.9%
O14127
10.5%
S12987
9.6%
F7561
 
5.6%
ValueCountFrequency (%)
2134804
25.0%
0134804
25.0%
1134804
25.0%
3134804
25.0%
ValueCountFrequency (%)
-134804
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common674020
62.5%
Latin404412
37.5%

Most frequent character per script

ValueCountFrequency (%)
e35560
 
8.8%
u35483
 
8.8%
J29681
 
7.3%
c29139
 
7.2%
a25495
 
6.3%
p22406
 
5.5%
A22093
 
5.5%
M18623
 
4.6%
n17771
 
4.4%
r17692
 
4.4%
Other values (12)150469
37.2%
ValueCountFrequency (%)
-134804
20.0%
2134804
20.0%
0134804
20.0%
1134804
20.0%
3134804
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1078432
100.0%

Most frequent character per block

ValueCountFrequency (%)
-134804
12.5%
2134804
12.5%
0134804
12.5%
1134804
12.5%
3134804
12.5%
e35560
 
3.3%
u35483
 
3.3%
J29681
 
2.8%
c29139
 
2.7%
a25495
 
2.4%
Other values (17)249054
23.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1221
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14707.37515
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:36.115747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile4000
Q18500
median13000
Q320000
95-th percentile30000
Maximum35000
Range34000
Interquartile range (IQR)11500

Descriptive statistics

Standard deviation8098.737341
Coefficient of variation (CV)0.5506582417
Kurtosis-0.198448233
Mean14707.37515
Median Absolute Deviation (MAD)5250
Skewness0.6617993948
Sum1982613000
Variance65589546.52
MonotocityNot monotonic
2021-04-19T09:54:36.254943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100009836
 
7.3%
120007244
 
5.4%
150007046
 
5.2%
200006873
 
5.1%
80004401
 
3.3%
350004317
 
3.2%
160004019
 
3.0%
180003699
 
2.7%
240003547
 
2.6%
60003402
 
2.5%
Other values (1211)80420
59.7%
ValueCountFrequency (%)
1000349
0.3%
10252
 
< 0.1%
10751
 
< 0.1%
11007
 
< 0.1%
11253
 
< 0.1%
ValueCountFrequency (%)
350004317
3.2%
349756
 
< 0.1%
349251
 
< 0.1%
349001
 
< 0.1%
348251
 
< 0.1%

mort_acc
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.878082253
Minimum0
Maximum31
Zeros51653
Zeros (%)38.3%
Memory size1.0 MiB
2021-04-19T09:54:36.410849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum31
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.19635659
Coefficient of variation (CV)1.16946773
Kurtosis3.580126873
Mean1.878082253
Median Absolute Deviation (MAD)1
Skewness1.503936043
Sum253173
Variance4.823982269
MonotocityNot monotonic
2021-04-19T09:54:36.535138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
051653
38.3%
122560
16.7%
218408
 
13.7%
314164
 
10.5%
410808
 
8.0%
57254
 
5.4%
64453
 
3.3%
72556
 
1.9%
81393
 
1.0%
9736
 
0.5%
Other values (19)819
 
0.6%
ValueCountFrequency (%)
051653
38.3%
122560
16.7%
218408
 
13.7%
314164
 
10.5%
410808
 
8.0%
ValueCountFrequency (%)
311
< 0.1%
301
< 0.1%
291
< 0.1%
271
< 0.1%
251
< 0.1%

open_acc
Real number (ℝ≥0)

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.15063351
Minimum0
Maximum62
Zeros3
Zeros (%)< 0.1%
Memory size1.0 MiB
2021-04-19T09:54:36.698033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q314
95-th percentile20
Maximum62
Range62
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.652662916
Coefficient of variation (CV)0.417255478
Kurtosis2.077010565
Mean11.15063351
Median Absolute Deviation (MAD)3
Skewness1.017473472
Sum1503150
Variance21.64727221
MonotocityNot monotonic
2021-04-19T09:54:36.837269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
913379
9.9%
1012949
9.6%
812384
 
9.2%
1111971
 
8.9%
710902
 
8.1%
1210484
 
7.8%
138780
 
6.5%
68506
 
6.3%
147484
 
5.6%
156034
 
4.5%
Other values (44)31931
23.7%
ValueCountFrequency (%)
03
 
< 0.1%
141
 
< 0.1%
2340
 
0.3%
31114
 
0.8%
43000
2.2%
ValueCountFrequency (%)
621
< 0.1%
532
< 0.1%
521
< 0.1%
511
< 0.1%
502
< 0.1%

pub_rec
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1368060295
Minimum0
Maximum54
Zeros118805
Zeros (%)88.1%
Memory size1.0 MiB
2021-04-19T09:54:36.970099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4632517901
Coefficient of variation (CV)3.386194248
Kurtosis2305.686782
Mean0.1368060295
Median Absolute Deviation (MAD)0
Skewness24.06241939
Sum18442
Variance0.214602221
MonotocityNot monotonic
2021-04-19T09:54:37.088828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0118805
88.1%
114477
 
10.7%
21071
 
0.8%
3261
 
0.2%
496
 
0.1%
544
 
< 0.1%
624
 
< 0.1%
713
 
< 0.1%
86
 
< 0.1%
112
 
< 0.1%
Other values (4)5
 
< 0.1%
ValueCountFrequency (%)
0118805
88.1%
114477
 
10.7%
21071
 
0.8%
3261
 
0.2%
496
 
0.1%
ValueCountFrequency (%)
541
< 0.1%
491
< 0.1%
112
< 0.1%
101
< 0.1%
92
< 0.1%

pub_rec_bankruptcies
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1092326637
Minimum0
Maximum8
Zeros120491
Zeros (%)89.4%
Memory size1.0 MiB
2021-04-19T09:54:37.208627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3258204726
Coefficient of variation (CV)2.982811748
Kurtosis17.5811771
Mean0.1092326637
Median Absolute Deviation (MAD)0
Skewness3.281809345
Sum14725
Variance0.1061589803
MonotocityNot monotonic
2021-04-19T09:54:37.312692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0120491
89.4%
114010
 
10.4%
2239
 
0.2%
337
 
< 0.1%
418
 
< 0.1%
64
 
< 0.1%
53
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
0120491
89.4%
114010
 
10.4%
2239
 
0.2%
337
 
< 0.1%
418
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
64
 
< 0.1%
53
 
< 0.1%
418
< 0.1%

purpose
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
debt_consolidation
80634 
credit_card
32804 
home_improvement
 
7403
other
 
5842
major_purchase
 
2298
Other values (8)
 
5823

Length

Max length18
Median length18
Mean length15.11227412
Min length3

Characters and Unicode

Total characters2037195
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowdebt_consolidation
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowdebt_consolidation
ValueCountFrequency (%)
debt_consolidation80634
59.8%
credit_card32804
24.3%
home_improvement7403
 
5.5%
other5842
 
4.3%
major_purchase2298
 
1.7%
small_business1359
 
1.0%
car1050
 
0.8%
medical889
 
0.7%
house675
 
0.5%
moving639
 
0.5%
Other values (3)1211
 
0.9%
2021-04-19T09:54:37.614057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation80634
59.8%
credit_card32804
24.3%
home_improvement7403
 
5.5%
other5842
 
4.3%
major_purchase2298
 
1.7%
small_business1359
 
1.0%
car1050
 
0.8%
medical889
 
0.7%
house675
 
0.5%
moving639
 
0.5%
Other values (3)1211
 
0.9%

Most occurring characters

ValueCountFrequency (%)
o266727
13.1%
d228955
11.2%
t207882
10.2%
i205522
10.1%
n171931
8.4%
c151044
7.4%
e147560
7.2%
_124549
 
6.1%
a122513
 
6.0%
s89043
 
4.4%
Other values (12)321469
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1912646
93.9%
Connector Punctuation124549
 
6.1%

Most frequent character per category

ValueCountFrequency (%)
o266727
13.9%
d228955
12.0%
t207882
10.9%
i205522
10.7%
n171931
9.0%
c151044
7.9%
e147560
7.7%
a122513
6.4%
s89043
 
4.7%
r84601
 
4.4%
Other values (11)236868
12.4%
ValueCountFrequency (%)
_124549
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1912646
93.9%
Common124549
 
6.1%

Most frequent character per script

ValueCountFrequency (%)
o266727
13.9%
d228955
12.0%
t207882
10.9%
i205522
10.7%
n171931
9.0%
c151044
7.9%
e147560
7.7%
a122513
6.4%
s89043
 
4.7%
r84601
 
4.4%
Other values (11)236868
12.4%
ValueCountFrequency (%)
_124549
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2037195
100.0%

Most frequent character per block

ValueCountFrequency (%)
o266727
13.1%
d228955
11.2%
t207882
10.2%
i205522
10.1%
n171931
8.4%
c151044
7.4%
e147560
7.2%
_124549
 
6.1%
a122513
 
6.0%
s89043
 
4.4%
Other values (12)321469
15.8%

revol_bal
Real number (ℝ≥0)

SKEWED

Distinct39861
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16800.48039
Minimum0
Maximum2568995
Zeros334
Zeros (%)0.2%
Memory size1.0 MiB
2021-04-19T09:54:37.778647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2964.3
Q17325
median12692
Q321121
95-th percentile38914.85
Maximum2568995
Range2568995
Interquartile range (IQR)13796

Descriptive statistics

Standard deviation20785.56609
Coefficient of variation (CV)1.2372007
Kurtosis2477.081979
Mean16800.48039
Median Absolute Deviation (MAD)6305
Skewness27.9368739
Sum2264771958
Variance432039757.8
MonotocityNot monotonic
2021-04-19T09:54:37.930390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0334
 
0.2%
742921
 
< 0.1%
565517
 
< 0.1%
888117
 
< 0.1%
685216
 
< 0.1%
933416
 
< 0.1%
897916
 
< 0.1%
1136316
 
< 0.1%
952916
 
< 0.1%
877516
 
< 0.1%
Other values (39851)134319
99.6%
ValueCountFrequency (%)
0334
0.2%
14
 
< 0.1%
27
 
< 0.1%
37
 
< 0.1%
47
 
< 0.1%
ValueCountFrequency (%)
25689951
< 0.1%
17467161
< 0.1%
17432661
< 0.1%
6946151
< 0.1%
6178381
< 0.1%

revol_util
Real number (ℝ≥0)

Distinct1068
Distinct (%)0.8%
Missing78
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean58.58012113
Minimum0
Maximum140.4
Zeros350
Zeros (%)0.3%
Memory size1.0 MiB
2021-04-19T09:54:38.088532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.4
Q142.8
median60.3
Q376.2
95-th percentile92.3
Maximum140.4
Range140.4
Interquartile range (IQR)33.4

Descriptive statistics

Standard deviation22.50388896
Coefficient of variation (CV)0.384155726
Kurtosis-0.5385026014
Mean58.58012113
Median Absolute Deviation (MAD)16.6
Skewness-0.3455529303
Sum7892265.4
Variance506.4250184
MonotocityNot monotonic
2021-04-19T09:54:38.237243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0350
 
0.3%
64.6256
 
0.2%
59.3252
 
0.2%
70.8252
 
0.2%
67.4251
 
0.2%
61.5250
 
0.2%
72250
 
0.2%
68.7248
 
0.2%
61.6248
 
0.2%
71.8247
 
0.2%
Other values (1058)132122
98.0%
ValueCountFrequency (%)
0350
0.3%
0.137
 
< 0.1%
0.246
 
< 0.1%
0.329
 
< 0.1%
0.431
 
< 0.1%
ValueCountFrequency (%)
140.41
< 0.1%
128.11
< 0.1%
127.61
< 0.1%
122.51
< 0.1%
120.22
< 0.1%

sub_grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
B4
10570 
B3
10289 
B2
9793 
C3
 
8172
C4
 
7864
Other values (30)
88116 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters269608
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA3
2nd rowB2
3rd rowB3
4th rowA3
5th rowB2
ValueCountFrequency (%)
B410570
 
7.8%
B310289
 
7.6%
B29793
 
7.3%
C38172
 
6.1%
C47864
 
5.8%
B17822
 
5.8%
C17646
 
5.7%
C27313
 
5.4%
C57135
 
5.3%
B55641
 
4.2%
Other values (25)52559
39.0%
2021-04-19T09:54:38.557639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b410570
 
7.8%
b310289
 
7.6%
b29793
 
7.3%
c38172
 
6.1%
c47864
 
5.8%
b17822
 
5.8%
c17646
 
5.7%
c27313
 
5.4%
c57135
 
5.3%
b55641
 
4.2%
Other values (25)52559
39.0%

Most occurring characters

ValueCountFrequency (%)
B44115
16.4%
C38130
14.1%
428554
10.6%
328160
10.4%
227478
10.2%
126970
10.0%
523642
8.8%
D20566
7.6%
A17679
6.6%
E9059
 
3.4%
Other values (2)5255
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter134804
50.0%
Decimal Number134804
50.0%

Most frequent character per category

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%
ValueCountFrequency (%)
428554
21.2%
328160
20.9%
227478
20.4%
126970
20.0%
523642
17.5%

Most occurring scripts

ValueCountFrequency (%)
Latin134804
50.0%
Common134804
50.0%

Most frequent character per script

ValueCountFrequency (%)
B44115
32.7%
C38130
28.3%
D20566
15.3%
A17679
13.1%
E9059
 
6.7%
F4392
 
3.3%
G863
 
0.6%
ValueCountFrequency (%)
428554
21.2%
328160
20.9%
227478
20.4%
126970
20.0%
523642
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII269608
100.0%

Most frequent character per block

ValueCountFrequency (%)
B44115
16.4%
C38130
14.1%
428554
10.6%
328160
10.4%
227478
10.2%
126970
10.0%
523642
8.8%
D20566
7.6%
A17679
6.6%
E9059
 
3.4%
Other values (2)5255
 
1.9%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
36 months
100422 
60 months
34382 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1348040
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 36 months
3rd row 36 months
4th row 36 months
5th row 36 months
ValueCountFrequency (%)
36 months100422
74.5%
60 months34382
 
25.5%
2021-04-19T09:54:38.806910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:38.884485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
months134804
50.0%
36100422
37.2%
6034382
 
12.8%

Most occurring characters

ValueCountFrequency (%)
269608
20.0%
6134804
10.0%
m134804
10.0%
o134804
10.0%
n134804
10.0%
t134804
10.0%
h134804
10.0%
s134804
10.0%
3100422
 
7.4%
034382
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter808824
60.0%
Space Separator269608
 
20.0%
Decimal Number269608
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
m134804
16.7%
o134804
16.7%
n134804
16.7%
t134804
16.7%
h134804
16.7%
s134804
16.7%
ValueCountFrequency (%)
6134804
50.0%
3100422
37.2%
034382
 
12.8%
ValueCountFrequency (%)
269608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin808824
60.0%
Common539216
40.0%

Most frequent character per script

ValueCountFrequency (%)
m134804
16.7%
o134804
16.7%
n134804
16.7%
t134804
16.7%
h134804
16.7%
s134804
16.7%
ValueCountFrequency (%)
269608
50.0%
6134804
25.0%
3100422
 
18.6%
034382
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1348040
100.0%

Most frequent character per block

ValueCountFrequency (%)
269608
20.0%
6134804
10.0%
m134804
10.0%
o134804
10.0%
n134804
10.0%
t134804
10.0%
h134804
10.0%
s134804
10.0%
3100422
 
7.4%
034382
 
2.6%

title
Categorical

HIGH CARDINALITY

Distinct32326
Distinct (%)24.0%
Missing5
Missing (%)< 0.1%
Memory size1.0 MiB
Debt consolidation
18582 
Debt Consolidation
 
9016
Credit card refinancing
 
6637
Consolidation
 
3552
debt consolidation
 
2929
Other values (32321)
94083 

Length

Max length40
Median length18
Mean length16.16875496
Min length2

Characters and Unicode

Total characters2179532
Distinct characters90
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26973 ?
Unique (%)20.0%

Sample

1st rowDebt Consolidation and Credit Transfer
2nd rowDebt Consolidation
3rd rowDebt consolidation
4th rowPay off other Installment loan
5th rowNo Regrets
ValueCountFrequency (%)
Debt consolidation18582
 
13.8%
Debt Consolidation9016
 
6.7%
Credit card refinancing6637
 
4.9%
Consolidation3552
 
2.6%
debt consolidation2929
 
2.2%
Other1846
 
1.4%
Home improvement1535
 
1.1%
consolidation1414
 
1.0%
Credit Card Consolidation1360
 
1.0%
Consolidation Loan1064
 
0.8%
Other values (32316)86864
64.4%
2021-04-19T09:54:39.273865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
consolidation48423
 
15.8%
debt47495
 
15.5%
credit24371
 
8.0%
card19567
 
6.4%
loan16622
 
5.4%
refinancing7175
 
2.3%
home6069
 
2.0%
payoff5687
 
1.9%
pay4812
 
1.6%
off4128
 
1.3%
Other values (9044)121713
39.8%

Most occurring characters

ValueCountFrequency (%)
o228622
 
10.5%
n191360
 
8.8%
i179827
 
8.3%
176577
 
8.1%
e170126
 
7.8%
t161182
 
7.4%
a149352
 
6.9%
d126176
 
5.8%
r93954
 
4.3%
l84019
 
3.9%
Other values (80)618337
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1767172
81.1%
Uppercase Letter222383
 
10.2%
Space Separator176577
 
8.1%
Decimal Number6642
 
0.3%
Other Punctuation5137
 
0.2%
Dash Punctuation1089
 
< 0.1%
Connector Punctuation198
 
< 0.1%
Close Punctuation110
 
< 0.1%
Open Punctuation80
 
< 0.1%
Math Symbol72
 
< 0.1%
Other values (3)72
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C65136
29.3%
D44617
20.1%
L15908
 
7.2%
P12244
 
5.5%
R9625
 
4.3%
O9400
 
4.2%
H7011
 
3.2%
I6884
 
3.1%
F6194
 
2.8%
M6133
 
2.8%
Other values (16)39231
17.6%
ValueCountFrequency (%)
o228622
12.9%
n191360
10.8%
i179827
10.2%
e170126
9.6%
t161182
9.1%
a149352
8.5%
d126176
7.1%
r93954
 
5.3%
l84019
 
4.8%
s83728
 
4.7%
Other values (16)298826
16.9%
ValueCountFrequency (%)
!1403
27.3%
/1257
24.5%
.985
19.2%
&486
 
9.5%
,471
 
9.2%
'219
 
4.3%
#103
 
2.0%
:73
 
1.4%
%57
 
1.1%
"56
 
1.1%
Other values (3)27
 
0.5%
ValueCountFrequency (%)
12139
32.2%
21559
23.5%
01262
19.0%
31152
17.3%
4205
 
3.1%
5103
 
1.6%
6102
 
1.5%
952
 
0.8%
836
 
0.5%
732
 
0.5%
ValueCountFrequency (%)
+62
86.1%
~5
 
6.9%
=2
 
2.8%
|2
 
2.8%
>1
 
1.4%
ValueCountFrequency (%)
)109
99.1%
]1
 
0.9%
ValueCountFrequency (%)
(77
96.2%
[3
 
3.8%
ValueCountFrequency (%)
176577
100.0%
ValueCountFrequency (%)
-1089
100.0%
ValueCountFrequency (%)
$68
100.0%
ValueCountFrequency (%)
_198
100.0%
ValueCountFrequency (%)
`3
100.0%
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1989555
91.3%
Common189977
 
8.7%

Most frequent character per script

ValueCountFrequency (%)
o228622
11.5%
n191360
 
9.6%
i179827
 
9.0%
e170126
 
8.6%
t161182
 
8.1%
a149352
 
7.5%
d126176
 
6.3%
r93954
 
4.7%
l84019
 
4.2%
s83728
 
4.2%
Other values (42)521209
26.2%
ValueCountFrequency (%)
176577
92.9%
12139
 
1.1%
21559
 
0.8%
!1403
 
0.7%
01262
 
0.7%
/1257
 
0.7%
31152
 
0.6%
-1089
 
0.6%
.985
 
0.5%
&486
 
0.3%
Other values (28)2068
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2179532
100.0%

Most frequent character per block

ValueCountFrequency (%)
o228622
 
10.5%
n191360
 
8.8%
i179827
 
8.3%
176577
 
8.1%
e170126
 
7.8%
t161182
 
7.4%
a149352
 
6.9%
d126176
 
5.8%
r93954
 
4.3%
l84019
 
3.9%
Other values (80)618337
28.4%

total_acc
Real number (ℝ≥0)

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.91342245
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Memory size1.0 MiB
2021-04-19T09:54:39.441944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q117
median23
Q331
95-th percentile46
Maximum105
Range103
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.10274204
Coefficient of variation (CV)0.4456530236
Kurtosis0.5393756326
Mean24.91342245
Median Absolute Deviation (MAD)7
Skewness0.7582357975
Sum3358429
Variance123.2708809
MonotocityNot monotonic
2021-04-19T09:54:39.585525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205146
 
3.8%
225111
 
3.8%
235071
 
3.8%
215038
 
3.7%
195025
 
3.7%
175000
 
3.7%
184984
 
3.7%
244932
 
3.7%
254687
 
3.5%
164639
 
3.4%
Other values (74)85171
63.2%
ValueCountFrequency (%)
210
 
< 0.1%
383
 
0.1%
4256
 
0.2%
5470
0.3%
6824
0.6%
ValueCountFrequency (%)
1051
< 0.1%
981
< 0.1%
881
< 0.1%
841
< 0.1%
831
< 0.1%

verification_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Verified
66138 
Not Verified
38959 
Source Verified
29707 

Length

Max length15
Median length12
Mean length10.6986217
Min length8

Characters and Unicode

Total characters1442217
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowVerified
3rd rowSource Verified
4th rowSource Verified
5th rowNot Verified
ValueCountFrequency (%)
Verified66138
49.1%
Not Verified38959
28.9%
Source Verified29707
22.0%
2021-04-19T09:54:39.886319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-19T09:54:39.976228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
verified134804
66.3%
not38959
 
19.1%
source29707
 
14.6%

Most occurring characters

ValueCountFrequency (%)
e299315
20.8%
i269608
18.7%
r164511
11.4%
V134804
9.3%
f134804
9.3%
d134804
9.3%
o68666
 
4.8%
68666
 
4.8%
N38959
 
2.7%
t38959
 
2.7%
Other values (3)89121
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1170081
81.1%
Uppercase Letter203470
 
14.1%
Space Separator68666
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
e299315
25.6%
i269608
23.0%
r164511
14.1%
f134804
11.5%
d134804
11.5%
o68666
 
5.9%
t38959
 
3.3%
u29707
 
2.5%
c29707
 
2.5%
ValueCountFrequency (%)
V134804
66.3%
N38959
 
19.1%
S29707
 
14.6%
ValueCountFrequency (%)
68666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1373551
95.2%
Common68666
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
e299315
21.8%
i269608
19.6%
r164511
12.0%
V134804
9.8%
f134804
9.8%
d134804
9.8%
o68666
 
5.0%
N38959
 
2.8%
t38959
 
2.8%
S29707
 
2.2%
Other values (2)59414
 
4.3%
ValueCountFrequency (%)
68666
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1442217
100.0%

Most frequent character per block

ValueCountFrequency (%)
e299315
20.8%
i269608
18.7%
r164511
11.4%
V134804
9.3%
f134804
9.3%
d134804
9.3%
o68666
 
4.8%
68666
 
4.8%
N38959
 
2.7%
t38959
 
2.7%
Other values (3)89121
 
6.2%

zip_code
Categorical

HIGH CARDINALITY

Distinct834
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
945xx
 
1643
750xx
 
1442
112xx
 
1431
606xx
 
1300
100xx
 
1189
Other values (829)
127799 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters674020
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st row782xx
2nd row481xx
3rd row809xx
4th row945xx
5th row281xx
ValueCountFrequency (%)
945xx1643
 
1.2%
750xx1442
 
1.1%
112xx1431
 
1.1%
606xx1300
 
1.0%
100xx1189
 
0.9%
900xx1180
 
0.9%
300xx1163
 
0.9%
070xx1125
 
0.8%
331xx1120
 
0.8%
917xx1050
 
0.8%
Other values (824)122161
90.6%
2021-04-19T09:54:40.658284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
945xx1643
 
1.2%
750xx1442
 
1.1%
112xx1431
 
1.1%
606xx1300
 
1.0%
100xx1189
 
0.9%
900xx1180
 
0.9%
300xx1163
 
0.9%
070xx1125
 
0.8%
331xx1120
 
0.8%
917xx1050
 
0.8%
Other values (824)122161
90.6%

Most occurring characters

ValueCountFrequency (%)
x269608
40.0%
060080
 
8.9%
148251
 
7.2%
343361
 
6.4%
243093
 
6.4%
942463
 
6.3%
739344
 
5.8%
434081
 
5.1%
532479
 
4.8%
832120
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number404412
60.0%
Lowercase Letter269608
40.0%

Most frequent character per category

ValueCountFrequency (%)
060080
14.9%
148251
11.9%
343361
10.7%
243093
10.7%
942463
10.5%
739344
9.7%
434081
8.4%
532479
8.0%
832120
7.9%
629140
7.2%
ValueCountFrequency (%)
x269608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common404412
60.0%
Latin269608
40.0%

Most frequent character per script

ValueCountFrequency (%)
060080
14.9%
148251
11.9%
343361
10.7%
243093
10.7%
942463
10.5%
739344
9.7%
434081
8.4%
532479
8.0%
832120
7.9%
629140
7.2%
ValueCountFrequency (%)
x269608
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII674020
100.0%

Most frequent character per block

ValueCountFrequency (%)
x269608
40.0%
060080
 
8.9%
148251
 
7.2%
343361
 
6.4%
243093
 
6.4%
942463
 
6.3%
739344
 
5.8%
434081
 
5.1%
532479
 
4.8%
832120
 
4.8%

Interactions

2021-04-19T09:53:44.404529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:44.585412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:44.780382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:44.981872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:45.170909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:45.359362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:45.555244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:45.745990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:45.931977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:46.104259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:46.271494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:46.467749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:46.662066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:46.854990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.032096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.224037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.407075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.594578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.785946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:47.980727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:48.176477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:48.371832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:48.565174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:48.734000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:48.907004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:49.093724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:49.278354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:49.466184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:49.634475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:49.821868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:50.010988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:50.205222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:51.014830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:51.558655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:52.422142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:52.606308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:53.002439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:53.606280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:53.789417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:54.009340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:54.187341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:54.362514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:54.745642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:57.155487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:57.733971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:58.964181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:53:59.485036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:59.678369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:53:59.858800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:02.802946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:03.593074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:03.976178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:04.542681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:04.737445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:05.133384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:05.317138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:10.598795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:10.767776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:10.966563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:11.169117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-04-19T09:54:14.483191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:14.668923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:14.848466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:15.064680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:15.275466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:15.479456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:15.692300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:15.902521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:16.115471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:16.307019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:16.506069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:16.696572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:16.906157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:17.130107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:17.338820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:17.553881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:17.737126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:17.926857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:18.136306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:18.321267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:18.507343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:18.684723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:18.870909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:19.265413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:19.441555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:19.611197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:19.799583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:19.987973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:20.169400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:20.346025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:20.540377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:20.734976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:20.928189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:21.123418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:21.341759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:21.545482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:21.741297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:21.929740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:22.116014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:22.286452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:22.473664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:22.664983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:22.852959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.039672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.227951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.411851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.599696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.790160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:23.972062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:24.167170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:24.346148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:24.520488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:24.697133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:24.868354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:25.055747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-19T09:54:25.238592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-19T09:54:40.794064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-19T09:54:41.096299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-19T09:54:41.390223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-19T09:54:41.700649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-19T09:54:42.058525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-19T09:54:25.911373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-19T09:54:27.977181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-19T09:54:28.958284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-19T09:54:29.343710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idmember_idloan_statusaddr_stateannual_incapplication_typedescdtiearliest_cr_lineemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinitial_list_statusinstallmentint_rateissue_dloan_amntmort_accopen_accpub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermtitletotal_accverification_statuszip_code
010148122NaNFully PaidTX96500.0IndividualBorrower added on 12/31/13 > Bought a new house, furniture, water softener, a second car, etc. Got our lives started and now a manageable monthly payment will help keep them going!<br>12.61Sep-20033 yearsSystems Engineer709.0705.0AMORTGAGEf373.947.62Dec-201312000.01.017.00.00.0debt_consolidation13248.055.7A336 monthsDebt Consolidation and Credit Transfer30.0Not Verified782xx
110149342NaNFully PaidMI55000.0IndividualBorrower added on 12/31/13 > Combining high interest credit cards to lower interest rate.<br>22.87Oct-198610+ yearsTeam Leadern Customer Ops & Systems734.0730.0BOWNw885.4610.99Dec-201327050.04.014.00.00.0debt_consolidation36638.061.2B236 monthsDebt Consolidation27.0Verified481xx
210119623NaNFully PaidCO130000.0IndividualNaN13.03Nov-199710+ yearsLTC719.0715.0BMORTGAGEf398.5211.99Dec-201312000.03.09.00.00.0debt_consolidation10805.067.0B336 monthsDebt consolidation19.0Source Verified809xx
310149577NaNFully PaidCA325000.0IndividualNaN18.55Nov-19945 yearsArea Sales Manager749.0745.0AMORTGAGEw872.527.62Dec-201328000.05.015.00.00.0debt_consolidation29581.054.6A336 monthsPay off other Installment loan31.0Source Verified945xx
410129454NaNFully PaidNC60000.0IndividualBorrower added on 12/31/13 > I would like to use this money to payoff existing credit card debt and use the remaining about to purchase a used car that is fuel efficient.<br>4.62Dec-20094 yearsProject Manager724.0720.0BRENTf392.8110.99Dec-201312000.00.015.00.00.0debt_consolidation7137.024.0B236 monthsNo Regrets18.0Not Verified281xx
510149526NaNCharged OffCO73000.0IndividualBorrower added on 12/31/13 > I had some water main break and sewer replacement that ran up my Credit cards. I want to consolidate the Credit cards pay off one loan and refurbish my bathrooms.<br><br> Borrower added on 12/31/13 > I had two water main breaks one sewer and one clean water and the cost ran up credit cards expenditures. I want to consolidate the credit cards with a set payment and upgrade my two bathrooms and water heater.<br><br> Borrower added on 12/31/13 > Consolidate credet cards and upgrade bathrooms.<br><br> Borrower added on 12/31/13 > Consolidate credit cards and upgrade two bathrooms.I have been at this job for six years and the job before this one for 24 years. This will make my finances easier to manage. It will provide more efficient bathroom equipment and water heater.<br>23.13Jun-19896 yearsStreet Operations Supervisor669.0665.0DMORTGAGEf730.7819.97Dec-201327600.04.010.00.00.0debt_consolidation27003.082.8D560 monthsConsolidation of debt and home improve.24.0Source Verified802xx
610224583NaNFully PaidNY90000.0IndividualNaN3.73Jun-200110+ yearsTeacher694.0690.0CMORTGAGEf384.6814.98Dec-201311100.01.09.00.00.0other6619.066.2C336 monthsOther12.0Not Verified103xx
710159584NaNFully PaidCA26000.0IndividualBorrower added on 12/31/13 > While being in college there were expenses that I had to make. At the moment it seemed easy to buy thing on credit, but now that I'm full-time employee paying all credit cards seem impossible and it'll be great to make one consolidated payment to one firm with knowing its for a set amount of months.<br>25.12Jan-20071 yearMedical Assistant674.0670.0CRENTf333.1413.98Dec-20139750.00.012.00.00.0debt_consolidation7967.052.8C136 monthsDebt Consilation28.0Not Verified927xx
810139658NaNFully PaidNM40000.0IndividualNaN16.94Oct-199810+ yearsOn road manager664.0660.0BRENTw407.4013.53Dec-201312000.00.07.02.00.0debt_consolidation5572.068.8B536 monthsDebt consolidation32.0Source Verified871xx
910149488NaNFully PaidTX39600.0IndividualBorrower added on 12/31/13 > Just bought a house, and would like a little extra funds to improve aspects of the house such as, duct work, electrical outlets, backyard, and other minor areas.<br>2.49Aug-19952 yearsSurgical Technician759.0755.0BMORTGAGEw157.1310.99Dec-20134800.00.03.00.00.0home_improvement4136.016.1B236 monthsFor The House8.0Source Verified782xx

Last rows

idmember_idloan_statusaddr_stateannual_incapplication_typedescdtiearliest_cr_lineemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinitial_list_statusinstallmentint_rateissue_dloan_amntmort_accopen_accpub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermtitletotal_accverification_statuszip_code
1347942367122NaNFully PaidCA900000.0IndividualNaN10.11Mar-198010+ yearsFremont Bank709.0705.0AMORTGAGEf736.896.62Jan-201324000.012.017.00.00.0debt_consolidation373687.061.5A236 monthsDebt Consolidation51.0Verified945xx
1347952298828NaNCharged OffIL70000.0IndividualBorrower added on 12/06/12 > just a little help to get over the hump<br>10.71Feb-19956 yearsJBS United684.0680.0BMORTGAGEw787.1014.09Jan-201323000.04.011.00.00.0debt_consolidation10660.053.3B536 monthsout of debt45.0Verified618xx
1347962375433NaNFully PaidKS110000.0IndividualNaN10.58Aug-199810+ yearsFirst Prebyterian Church679.0675.0BMORTGAGEf843.6813.11Jan-201325000.04.010.00.00.0debt_consolidation4070.075.4B436 monthsDebt consolidation19.0Verified665xx
1347972365716NaNFully PaidCA203000.0IndividualBorrower added on 12/05/12 > To transfer higher-rate credit cards to this lower-rate account.<br>9.74Sep-19995 yearsNaN694.0690.0ARENTw666.828.90Jan-201321000.01.010.00.00.0credit_card14161.028.1A536 monthsLending Club Loan27.0Source Verified920xx
1347982375143NaNFully PaidTX35000.0IndividualBorrower added on 12/05/12 > Debt Consolidation<br>10.94Sep-1998NaNNaN724.0720.0BMORTGAGEw377.2611.14Jan-201311500.01.011.00.00.0debt_consolidation8158.034.8B236 monthsMy Consolidation16.0Verified783xx
1347992334898NaNFully PaidCA85000.0IndividualBorrower added on 12/05/12 > pay off credit card debt<br><br> Borrower added on 12/10/12 > pay credit card debt<br><br> Borrower added on 12/12/12 > credit card debt<br>21.70Aug-199710+ yearslocal 729734.0730.0BMORTGAGEw341.2210.16Jan-201316000.03.010.00.00.0credit_card8921.054.7B160 monthslending club loan28.0Verified910xx
1348002375068NaNFully PaidNJ55500.0IndividualNaN23.48Jun-19911 yearPomptonian food service company669.0665.0DRENTf657.5418.75Jan-201318000.00.015.00.00.0debt_consolidation13102.082.1D336 monthsconsolidation38.0Verified088xx
1348012374791NaNFully PaidTX158000.0IndividualBorrower added on 12/07/12 > I'm wanting to get this consolidation loan to help my cash flow and make one payment each month. I have a great income and hope to get the note payed off much quicker then the 36 month terms, so I can get back to saving again. Thanks<br>25.54May-199010+ yearsNaN679.0675.0BRENTf565.6212.12Jan-201317000.04.07.00.00.0debt_consolidation5896.057.8B336 monthsDEBT CONSOLIDATION19.0Verified781xx
1348022301035NaNFully PaidCA200000.0IndividualNaN13.81Aug-20009 yearsdirect telecom inc709.0705.0BMORTGAGEf1048.0612.12Jan-201331500.03.013.00.00.0credit_card31860.079.9B336 monthsmy cc loan24.0Verified915xx
1348032300581NaNFully PaidNY84000.0IndividualBorrower added on 12/04/12 > Just let debt get a little out of control. Feel it would be much easier to automatically withdraw 1 payment each month from my checking account to pay off my debt instead of paying 4 different locations and having to remember debts due dates.<br>25.86Jul-199910+ yearsTrugreen734.0730.0ARENTf876.137.90Jan-201328000.00.07.00.00.0debt_consolidation33601.068.2A436 monthsConsolidation10.0Verified122xx